Tree photosynthetic yield and radial growth dynamic response monitoring and time delay analysis method

Abstract

The invention belongs to the technical field of ecological monitoring and forest management, and discloses a tree photosynthetic yield and radial growth dynamic response monitoring and time-delay analysis method which comprises the following steps: performing high-resolution dynamic monitoring on radial growth of a sample tree through a micro-tree core technology, collecting a micro-tree core sample, and determining dynamic change of cambium cells; constructing a photosynthetic productivity model, and calculating the daily photosynthetic yield; carrying out normalization processing on radial growth and photosynthetic yield data, fitting a dynamic relationship between the radial growth and the photosynthetic yield by utilizing linear and nonlinear equations, and analyzing and discussing the instantaneity or hysteresis influence of the photosynthetic yield on the radial growth in combination with a time sequence; and judging the response relationship between the photosynthetic yield and the radial growth by adjusting the correlation parameters and the dynamic change trend of the fitting model. According to the method, the research precision and efficiency are remarkably improved, and an important technical support is provided for dynamic research and management of a forest ecosystem.

Description

Technical Field

[0001] The present invention belongs to the technical field of ecological monitoring and forest management, and specifically provides a method for monitoring and time-lag analysis of the dynamic response of tree photosynthetic production and radial growth, which is used to explore whether the impact of tree photosynthetic production on radial growth is immediate or lagged. Background Art

[0002] The radial growth and photosynthesis of trees are key processes in the carbon cycle of forest ecosystems. Radial growth realizes carbon accumulation through the division and lignification of cambial cells, while photosynthesis provides energy and carbon sources for growth. Research shows that the impact of photosynthetic products (photosynthetic production) on radial growth may have two mechanisms: immediate and lagged. The immediate mechanism believes that photosynthetic production can be used for radial growth on the same day; the lagged mechanism believes that the products need to go through synthesis and distribution, and the impact may be delayed for several days or even weeks. However, the dynamic characteristics of the relationship between the two on a short time scale are not yet clear.

[0003] Existing research methods have problems such as insufficient monitoring resolution, high complexity of data models, and limitations of measurement techniques. Traditional annual ring analysis and trunk dendrometer monitoring are limited by time resolution and environmental interference, and it is difficult to capture the dynamic changes of the cambium. The photosynthesis rate is affected by the rapid changes of environmental factors. Existing models are mostly based on large-scale data and do not fully consider the physiological characteristics of different tree species. In addition, many measurement methods cause irreversible damage to trees, limiting the feasibility of long-term dynamic monitoring.

[0004] In recent years, the micro-core technique has provided the possibility for high-resolution research. By regularly extracting micro-core samples and combining high-precision photosynthesis measurement with environmental data, the relationship between cambial activity and photosynthetic production can be dynamically captured. However, the current research on the immediate or lagged impact of the two still needs to further optimize the technical scheme to improve the accuracy. The present invention proposes an efficient systematic method, which combines the micro-core technique and precise model analysis to quantify the dynamic impact of photosynthetic production on radial growth and provide scientific support for the research and management of forest ecosystems. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for monitoring and time-lag analysis of the dynamic response of tree photosynthetic production and radial growth to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A method for monitoring and time-lag analysis of the dynamic response of tree photosynthetic production and radial growth, comprising the following steps:

[0008] S1: Select target tree species and sample trees

[0009] Select the target tree species, mark the sample trees as the objects for monitoring and sampling, and record the diameter at breast height (DBH) of the sample trees;

[0010] S2: Collection of tree radial growth data

[0011] Monitor the radial growth of the sample trees with high resolution by the micro-core method;

[0012] During the growing season, extract the micro-core samples of the sample trees at fixed time intervals. The sampling points are set at the DBH of the sample trees;

[0013] Fix the micro-core samples in FAA solution and perform standardization processes, including softening, dehydration, sectioning and staining;

[0014] Determine the dynamics of cambial cell division, differentiation and lignification, and record the start and end times and dynamic changes of radial growth;

[0015] Combined with the DBH data of the sample trees, convert the length measured by the micro-core into the tree basal area;

[0016] S3: Collection of tree photosynthetic production data

[0017] Measure the light response curve of the leaves of the sample tree species using a fully automatic photosynthetic fluorescence measurement system;

[0018] Construct a photosynthetic productivity model of the sample tree species by combining the light response curve of the leaves of the sample tree species with the environmental data in the area, and calculate the daily photosynthetic production;

[0019] The photosynthetic productivity model should consider the light-temperature coupling effect and the characteristics of the canopy layer structure, and collect the data required for constructing the photosynthetic productivity model under different light, temperature and canopy heights;

[0020] S4: Data analysis and dynamic relationship analysis

[0021] Normalize the radial growth data and photosynthetic production data, and calculate the relative basal area increment and relative cumulative photosynthetic production respectively;

[0022] Use linear and non-linear equations to fit the dynamic relationship between the relative basal area increment and the relative cumulative photosynthetic production, and evaluate the correlation and dynamic characteristics;

[0023] Perform time series analysis on the radial growth data and photosynthetic production data to identify the corresponding time relationship between the two;

[0024] S5: Result determination

[0025] According to the adjusted R 2 value of the photosynthetic productivity model and the trend of the time series broken line chart, determine the relationship between photosynthetic production and radial growth;

[0026] If R is adjusted 2 The closer the value is to 1, and the trend of the photosynthetic yield in the time series line chart is consistent with the radial growth curve, it is determined that there is an immediate relationship between the photosynthetic yield and the radial growth; if there is a significant lag phenomenon, it is determined that there is a lag effect between the photosynthetic yield and the radial growth;

[0027] Furthermore, the S1 further includes: classifying the sample trees according to the diameter at breast height of the target tree species to ensure that the number of samples in each diameter class is balanced and reasonably distributed.

[0028] Furthermore, the time interval for collecting the micro-core samples is 5 - 10 days, and the collection time is fixed in the morning or evening to reduce systematic errors.

[0029] Furthermore, the photosynthetic productivity model optimizes the calculation of the photosynthetic yield by considering the light-temperature coupling effect and the characteristics of the canopy layer, and collects the data required to construct the photosynthetic productivity model under different light, temperature, and canopy height.

[0030] Furthermore, when fitting the dynamic relationship in the S4, a linear relationship model or a non-linear equation is used, and the best fitting model is selected by adjusting the R 2 value to determine the optimal dynamic relationship between the photosynthetic yield and the radial growth of the tree.

[0031] Furthermore, the normalization process in the S4 adopts the following formula:

[0032]

[0033] where LG T is the relative value of the photosynthetic yield of the tree at time T, G T is the actually measured value of the photosynthetic yield of the tree at T, G max is the maximum value of the photosynthetic yield of the tree during the observation period; LR T is the relative value of the radial growth at time T, R T is the actually measured value of the radial growth at T, R max is the maximum value of the radial growth during the observation period.

[0034] Furthermore, the conversion formula for converting the length measured by the micro-core into the cross-sectional area of the tree by combining the diameter at breast height data of the sample tree in the S2 is:

[0035] R = π × (r 2 + r × DBH)

[0036] where R is the increment of the radial cross-sectional area of the sample tree, r is the radial growth of the sample tree, DBH is the diameter at breast height of the sample tree, and π is the pi.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows: Through high-resolution dynamic monitoring and precise analysis models, the present invention significantly improves the accuracy and efficiency of the research on the relationship between tree photosynthetic production and radial growth. At the same time, the technical principle of the present invention is not only applicable to forest ecosystem research, but also can be extended to other fields of plant ecology and functional trait research, providing important technical support for climate change adaptation research and forest sustainable management. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 FIG. is a schematic flow chart of a method for monitoring and time-delay analysis of the dynamic response of tree photosynthetic production and radial growth provided by an embodiment of the present invention;

[0039] Figure 2 FIG. is a correlation diagram of the relative basal area increment and relative cumulative photosynthetic production of the target tree species Pinus koraiensis provided by an embodiment of the present invention;

[0040] Figure 3 FIG. is a line graph showing the changes of the relative basal area increment and relative cumulative photosynthetic production of the target tree species Pinus koraiensis over time provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0042] Embodiment

[0043] The target tree species is Pinus koraiensis in the broad-leaved Korean pine forest in Changbai Mountain. As Figure 1 shown, a method for monitoring and time-delay analysis of the dynamic response of tree photosynthetic production and radial growth includes the following steps:

[0044] S1: Select the target tree species and sample trees

[0045] Select the target tree species, mark the sample trees as the objects for monitoring and sampling, and record the diameter at breast height of the sample trees. The sample trees should have the characteristics of healthy growth, no pests and diseases, and be able to represent the typical growth state of the target tree species in the research area to ensure the reliability and scientificity of the monitoring data.

[0046] Optionally, classify the sample trees according to the diameter at breast height (DBH) of the target tree species, and divide the target tree species into different grades based on DBH. The division of DBH grades should be based on the growth characteristics of the trees, the environmental conditions of the ecological region, and the research requirements. The division scale is preferably 5 - 10 cm to comprehensively cover the individual growth characteristics within different DBH ranges, improve the representativeness of the samples and the scalability of the data.

[0047] Furthermore, for the divided DBH grades, mark the sample trees within each grade to ensure that the sample numbers of each DBH grade are balanced and reasonably distributed, providing a solid foundation for subsequent dynamic monitoring and data analysis.

[0048] S2: Collection of tree radial growth data

[0049] Conduct high-resolution monitoring of the radial growth of the sample trees through the micro-core method. Specifically, it includes:

[0050] S21: During the growing season, extract micro-core samples of the target sample trees at fixed time intervals. The sampling points are set at the breast height of the sample trees, preferably at 1.3 meters above the ground, and a spiral distribution is used to reduce damage to the sample trees.

[0051] The time interval for collecting the micro-core samples depends on the needs of research and other purposes, and is preferably 5 - 10 days.

[0052] The collection time of the micro-core samples is fixed in the morning or evening to avoid systematic errors. The advantage of collecting in the morning is that the photosynthesis products of the sample trees on the same day have not yet been produced, and the calculation of the cumulative photosynthetic yield can be accurately traced back to the previous day; collecting in the evening is carried out after the photosynthesis products of the sample trees on the same day are basically completed, and the cumulative calculation of the photosynthetic yield is up to the same day. By reasonably arranging the sampling time, the time matching between the radial growth data and the photosynthetic yield data can be ensured, improving the scientificity and accuracy of the analysis results.

[0053] S22: The collected micro-core samples should be immediately fixed in FAA solution (70% ethanol: formaldehyde: acetic acid = 9:0.5:0.5), and standardized treatment should be carried out in the laboratory, including softening, dehydration, sectioning, and staining. After the micro-core samples are fixed, laboratory standardized treatment is adopted to measure the division, differentiation, and lignification dynamics of the cambial cells, and record the time and dynamic changes of the start and end of tree radial growth.

[0054] S23: Combine the radial growth data measured from the micro-core samples with the breast height data of the sample trees, and convert it into the tree basal area. The conversion formula is:

[0055] R = π×(r 2 + r×DBH)

[0056] Wherein, R is the radial cross-sectional area increment of the sample tree, r is the radial growth of the sample tree, DBH is the diameter at breast height of the sample tree, and π is the pi.

[0057] S3: Collection of tree photosynthetic yield data

[0058] Use a fully automatic photosynthetic fluorescence measurement system well-known in the art to measure the leaf light response curve of the sample tree species. Combine the light response curve of the sample tree species' leaves with the environmental data in the area to construct a photosynthetic productivity model of the sample tree species, and calculate the daily photosynthetic yield of the sample tree.

[0059] Among them, the photosynthetic productivity model is a well-known technology. It uses mathematical and ecological methods to simulate and quantify the plant photosynthesis process, and is usually used to estimate the primary productivity of an ecosystem or the photosynthetic yield of a specific plant.

[0060] Preferably, the leaf light response curve of the sample tree species is measured under different temperature conditions.

[0061] Preferably, the environmental data includes photosynthetically active radiation and temperature data.

[0062] Preferably, the photosynthetic productivity model of the sample tree species considers the light-temperature coupling effect and the canopy layer structure characteristics, and collects the data required to construct the photosynthetic productivity model under different light, temperature, and canopy heights. When considering the light-temperature coupling effect, the rate of photosynthesis is jointly regulated by light intensity and temperature, and their interaction (coupling effect) can significantly change the process and yield of photosynthesis. When considering the canopy layer structure characteristics, the canopy layer is the main place for photosynthesis, and the structure of the canopy layer significantly affects the light energy distribution, gas exchange, and overall productivity.

[0063] S4: Data analysis and dynamic relationship analysis

[0064] Perform time series analysis on the radial growth data and photosynthetic yield data to identify the time lag effect between the two, so as to determine the immediate or lagged effect of photosynthetic yield on radial growth. Specifically include:

[0065] S41: Normalize the radial growth data (cross-sectional area increment) and cumulative photosynthetic yield data of the sample tree. By dividing the cross-sectional area increment and cumulative photosynthetic yield by their maximum values respectively, the relative cross-sectional area increment and relative cumulative photosynthetic yield are obtained. The normalization formula is:

[0066]

[0067] Wherein, LG T is the relative value of the tree photosynthetic yield at time T, G T is the actually measured value of the tree photosynthetic yield at T, G maxis the maximum value of the photosynthetic production of the tree during the observation period; LR T is the relative value of radial growth at time T, R T is the actual measured value of radial growth at T, R max is the maximum value of radial growth during the observation period.

[0068] Normalization eliminates the scale effect between different samples and ensures data comparability.

[0069] S42: Based on the normalized data, use linear and non-linear equations to fit the dynamic relationship between the relative basal area increment and the relative cumulative photosynthetic production, and evaluate the correlation and dynamic characteristics between the two, see Figure 2 .

[0070] Optionally, the fitting models include linear relationship models and non-linear equations such as power functions. Select the model with the highest adjusted R 2 value (adjustment coefficient) to determine the best fitting relationship between the two.

[0071] S43: Take the mean of the relative basal area increment and relative cumulative photosynthetic production data of each sample tree, and draw a line graph showing the change over time to visually display the dynamic change trends of the two, see Figure 3 .

[0072] S5: Result determination

[0073] Based on the data analysis results, determine the relationship between photosynthetic production and radial growth by adjusting the R 2 value and the trend of the time series line graph. Specifically, it includes:

[0074] S51: Observe the adjusted R 2 value of the linear or non-linear fitting model. If the adjusted R 2 value is closer to 1, it indicates that the correlation between photosynthetic production and radial growth is higher and the model fitting effect is better.

[0075] S52: Compare the trends of the line graphs showing the changes in relative basal area increment and relative cumulative photosynthetic production over time. If the trends of the two curves are basically the same, that is, the change in relative basal area increment is synchronized with the cumulative dynamics of photosynthetic production, it can be determined that there is an instantaneous relationship between the two.

[0076] S53: If the adjusted R 2 value is low or there is a significant lag in the trend of the line graph, it indicates that there is a lag in the effect of photosynthetic production on radial growth.

[0077] The above embodiments have elaborated in detail the specific steps and technical features of the present invention, demonstrating the monitoring and analysis process of the impact of photosynthetic yield on radial growth dynamics. The method of the present invention has the advantages of high resolution, reliable data, and accurate analysis model, and can be widely applied to the research of forest ecosystems, and can be extended to other fields of plant ecology and functional trait research.

[0078] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring and time-lag analysis of the dynamic response of photosynthetic yield and radial growth of trees, characterized in that: The following steps are involved: S1: Select target tree species and sample trees Select target tree species, mark sample trees as monitoring and sampling objects, and record the DBH of sample trees; S2: Collection of tree radial growth data High-resolution monitoring of radial growth of sample trees by micro-tree core method; During the growing season, micro-core samples are extracted from sample trees at fixed intervals, and the sampling points are set at the breast diameter of the sample trees. The micro-tree core samples were fixed in FAA solution and subjected to standardized processing, including softening, dehydration, sectioning and staining; Determine the division, differentiation and lignification dynamics of cambium cells, and record the start and end time of radial growth and its dynamic changes; Combined with the DBH data of the sample trees, the length measured by the micro-tree core was converted into the cross-sectional area of ​​the tree; S3: Collection of tree photosynthetic yield data The light response curves of leaves of sample tree species were measured using a fully automatic photosynthetic fluorescence measurement system; The photosynthetic productivity model of the sample tree species was constructed by combining the light response curve of the leaves of the sample tree species with the environmental data in the region, and the daily photosynthetic output was calculated. The photosynthetic productivity model should take into account the light-temperature coupling effect and the characteristics of the canopy structure, and collect the data required to build the photosynthetic productivity model under different light, temperature, and canopy height; S4: Data analysis and dynamic relationship analysis The radial growth data and photosynthetic yield data were normalized, and the relative basal area increment and relative cumulative photosynthetic yield were calculated respectively; The dynamic relationship between relative basal area increment and relative cumulative photosynthetic yield was fitted using linear and nonlinear multiple equations to evaluate the correlation and dynamic characteristics; Time series analysis was performed on radial growth data and photosynthetic yield data to identify the corresponding temporal relationship between the two; S5: Result determination Adjustment of R according to photosynthetic productivity model 2 The relationship between photosynthetic yield and radial growth was determined by using the values ​​and the trend of the time series line graph; If you adjust R 2 The closer the value is to 1, and the photosynthetic yield in the time series line graph is consistent with the radial growth curve trend, there is an immediate relationship between the fixed photosynthetic yield and the radial growth; if there is a significant hysteresis, there is a hysteresis effect between the fixed photosynthetic yield and the radial growth.

2. The method for monitoring and time-lag analysis of the dynamic response of photosynthetic yield and radial growth of trees according to claim 1, characterized in that: The S1 also includes: classifying the sample trees according to the diameter at breast height of the target tree species to ensure that the number of samples of each diameter at breast height grade is balanced and reasonably distributed.

3. The method for monitoring and time-lag analysis of the dynamic response of photosynthetic yield and radial growth of trees according to claim 1, characterized in that: The time interval for collecting the micro-tree core samples is 5-10 days, and the collection time is fixed in the morning or evening to reduce systematic errors.

4. The method for monitoring and time-lag analysis of the dynamic response of photosynthetic yield and radial growth of trees according to claim 1, characterized in that: The photosynthetic productivity model optimizes and calculates photosynthetic yield by considering the light-temperature coupling effect and canopy characteristics, and collects data required for constructing the photosynthetic productivity model under different light, temperature, and canopy heights.

5. The method for monitoring and time-lag analysis of the dynamic response of photosynthetic yield and radial growth of trees according to claim 1, characterized in that: When fitting the dynamic relationship in S4, several linear relationship models or nonlinear equations are used to adjust R 2 The best fitting model was screened and the optimal dynamic relationship between tree photosynthetic production and radial growth was determined.

6. The method for monitoring and time-lag analysis of the dynamic response of photosynthetic yield and radial growth of trees according to claim 1, characterized in that: The normalization process in S4 adopts the following formula: Among them, LG T is the relative value of the photosynthetic yield of trees at time T, G T is the actual measured value of tree photosynthetic yield at time T, G max is the maximum photosynthetic yield of trees during the observation period; LR T is the relative value of radial growth at time T, R T is the actual measured value of radial growth at time T, R max is the maximum radial growth value during the observation period.

7. The method for monitoring and time-lag analysis of the dynamic response of photosynthetic yield and radial growth of trees according to claim 1, characterized in that: In S2, the conversion formula for converting the length measured by the micro-tree core into the cross-sectional area of ​​the tree in combination with the breast diameter data of the sample tree is: R=π×(r 2 +r×DBH) Among them, R is the radial cross-sectional area increment of the sample tree, r is the radial growth of the sample tree, DBH is the breast height diameter of the sample tree, and π is pi.

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